manipulation of signaling thresholds in “engineered stem cell niches” identifies design criteria...

Manipulation of Signaling Thresholds in ‘‘EngineeredStem Cell Niches’’ Identifies Design Criteria forPluripotent Stem Cell ScreensRaheem Peerani1,2, Kento Onishi1, Alborz Mahdavi1,2, Eugenia Kumacheva1,2,3, Peter W. Zandstra1,2*

1 Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada, 2 Department of Chemical Engineering and Applied Chemistry,

University of Toronto, Toronto, Ontario, Canada, 3 Department of Chemistry, University of Toronto, Toronto, Ontario, Canada

Abstract

In vivo, stem cell fate is regulated by local microenvironmental parameters. Governing parameters in this stem cell nicheinclude soluble factors, extra-cellular matrix, and cell-cell interactions. The complexity of this in vivo niche limits analysesinto how individual niche parameters regulate stem cell fate. Herein we use mouse embryonic stem cells (mESC) and micro-contact printing (mCP) to investigate how niche size controls endogenous signaling thresholds. mCP is used to restrict colonydiameter, separation, and degree of clustering. We show, for the first time, spatial control over the activation of the Januskinase/signal transducer and activator of transcription pathway (Jak-Stat). The functional consequences of this niche-size-dependent signaling control are confirmed by demonstrating that direct and indirect transcriptional targets of Stat3,including members of the Jak-Stat pathway and pluripotency-associated genes, are regulated by colony size. Modelingresults and empirical observations demonstrate that colonies less than 100 mm in diameter are too small to maximizeendogenous Stat3 activation and that colonies separated by more than 400 mm can be considered independent from eachother. These results define parameter boundaries for the use of ESCs in screening studies, demonstrate the importance ofcontext in stem cell responsiveness to exogenous cues, and suggest that niche size is an important parameter in stem cellfate control.

Citation: Peerani R, Onishi K, Mahdavi A, Kumacheva E, Zandstra PW (2009) Manipulation of Signaling Thresholds in ‘‘Engineered Stem Cell Niches’’ IdentifiesDesign Criteria for Pluripotent Stem Cell Screens. PLoS ONE 4(7): e6438. doi:10.1371/journal.pone.0006438

Editor: Jean Peccoud, Virginia Tech, United States of America

Received March 31, 2009; Accepted June 30, 2009; Published July 30, 2009

Copyright: � 2009 Peerani et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permitsunrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work is funded by the CIHR Grant # RMF-72557 and NSERC (Steacie and Discovery). RP is a recipient of a NSERC PGS D Post-graduate Scholarship.PWZ is the Canada Research Chair in Stem Cell Bioengineering. The funders had no role in study design, data collection and analysis, decision to publish, orpreparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected]

Introduction

In vivo, embryogenesis is a highly orchestrated process involving

the interaction of several signaling networks that produce local

morphogenetic cues that determine cell fate and tissue organiza-

tion[1]. Embryonic stem cells (ESCs) which are derived from the

inner cell mass (ICM) of the embryo, are capable of recapitulating

some of the early events of embryogenesis and have been shown to

be capable of differentiation into many adult cell types in vitro[2–4].

However, this promising capability of ESCs is often offset by the

fact that ESC cultures can be highly heterogeneous, over resulting

in low yields of target cell types upon differentiation. This in vitro

situation can be contrasted to embryogenesis where cell fate and

spatial location appear tightly regulated. Motivated to address this

disparity, we set out to quantitatively characterize the parameters

that govern the spatially mediated activation of signaling and cell

fate in mouse ESC (mESC) cultures using mathematical modeling

and experimentation.

Recently, it has been shown that micro-patterning stem cells

cultures in two- and three-dimensions can regulate typically

uncontrolled ESC culture parameters such as colony size, distance

between colonies, ECM substrate, and cell-cell interactions [5–7].

High-throughput platforms have also screened the effect of various

extra-cellular matrix (ECM) and soluble growth factors on stem

cell differentiation[8–11]. Despite the increase in use of micro-

scale approaches to stem cell bioengineering, parameters which

govern the design of micropatterned stem cell cultures, namely

colony size and separation, have not been investigated for their

effects on endogenous signaling, a parameter that could be

important for the control of cell specification and for interpreting

the effects of test conditions on pluripotent cell fate.

In this study, we investigate whether micro-patterning mESC

cultures directly modulates paracrine signaling through the Janus

kinase – signal transducer and activator of transcription (Jak-Stat)

pathway. This pathway is activated by the interleukin-6 (IL-6) family

of cytokines including leukemia inhibitory factor (LIF) and is

typically required for the derivation and maintenance of mESCs in

vitro[12–14]. Receptor-ligand binding results in the phosphoryla-

tion of the tyrosine-705 residue of signal transducer and activator

of transcription 3 (pStat3) by receptor-associated Janus Kinases

(Jaks), followed by pStat3 translocation to the nucleus[15–17].

Direct transcriptional targets of pStat3 include members of the

Jak-Stat pathway, namely, gp130, Stat3, suppressor of cytokine

signaling 3 (Socs3), and LIF receptor (LIFR)[18]. Pathway-

associated targets include c-myc[19], Jumonji domain containing

protein 1a (Jmjd1a)[20,21], heterochromatin protein 1 (HP1)[22]

and DNA methyltransferase 1 (DNMT1)[23], and indirect targets

include pluripotency-associated core transcriptional network genes

PLoS ONE | www.plosone.org 1 July 2009 | Volume 4 | Issue 7 | e6438

such as Oct-4[24], Nanog[25], Sox2[26], Kruppel-like factor 4

(Klf4)[27], and Sall-4[28]. Measurement of these targets provide a

sensitive indication of the level of functional activation of the Jak-

Stat pathway.

In previous studies, we have used in silico modeling and

experimental observation to demonstrate that the gp130-Jak-Stat

pathway acts in a positive feedback loop that controls transcrip-

tional expression of LIF signaling components[18,29]. This loop

confers mESCs with a sensitivity towards the concentration of

exogenous LIF such that differentiation-inhibitory and differenti-

ation-permissive conditions occur over small changes in LIF

concentration[18]. We now specifically test whether colony size-

mediated control can be used to regulate Jak/Stat activation. Our

approach was to first develop a mathematical model to predict

how paracrine signaling thresholds are produced in uncontrolled

ESC cultures and how they can be modulated by micro-patterning

cultures. We then validated this model by measuring local pStat3

activation levels in both culture systems. Using the model, we were

successfully able to predict how Stat3 activation is modulated by

three micro-fabrication parameters: colony size, colony separation,

and degree of clustering. Modeling results and empirical

observations demonstrate that colonies less than 100 mm in

diameter are too small to maximize endogenous Stat3 activation

and that colonies separated by more than 400 mm can be

considered independent from each other. These results define

parameter boundaries for the use of ESCs in screening studies,

demonstrate the importance of context in stem cell responsiveness

to exogenous cues, and suggest that niche size is an important

parameter in stem cell fate control.

Model DevelopmentAutocrine and paracrine signaling provide two means for cells to

probe their micro-environment and communicate with other cells.

In particular, autocrine loops have been proposed to act as a means

of ‘‘cell sonar’’ whereby by cells can probe their microenvironment

and respond based on the capture of self-secreted ligands[30].

Likewise, paracrine signals seem to be one of the primary means of

induction during development[1]. The model described in this

paper extends a stochastic model developed previously that predicts

the fraction of autocrine and paracrine trajectories captured by a

single cell in cell culture assays[31,32]. In this previous work, a

general solution to ligand lifetime and spatial trapping distribution is

calculated based on the diffusivity of the ligand (D), single-cell

parameters such as total receptor number (Rt), cell radius (rcell),

binding affinity (kon and koff), and the single-cell trapping efficiency

(k). Under the infinite media height model, the fraction of autocrine

trajectories (Pau) was shown to be independent of total cell density

and medium height, while the trapping density of paracrine

trajectories [p(r)] had radial (r) and total cell density (s)

dependencies (Fig. S1). The parameter r is the distance between

cells. The major equations provided by the earlier modeling work

are the capture probabilities of autocrine and paracrine ligands:

Autocrine : Pau~vDa

vDaz4

p

ð1Þ

paracrine : p(r)~2keff

pD

ð?

0

x2

(keff r=D)2zx2K0(x)dx ð2Þ

where Da =krcell/D is the Damkohler number (dimensionless

reaction/diffusion ratio), Ko(x) is the modified zeroth order Bessel

function of the second kind which allows for p(r) to decay

exponentially with increasing r, and v is the internalization ratio

and is equal to ke/(ke+koff) where ke and koff and the first order rate

constants for endocytosis and ligand-receptor dissociation respec-

tively. keff is the effective single-cell trapping efficiency for paracrine

trajectories and is dependent upon cell density and the Damkohler

number:

keff ~ks

1zpDa=4ð3Þ

The rate constant, k, refers to the single cell trapping efficiency

and is equal to:

k~konRt

prcell2Na

ð4Þ

In this study, equations (1)–(4) were modified to incorporate the

following features. First, receptor ligand complex number per cell

(Cn) is calculated for each cell by summing the autocrine

trajectories with the paracrine trajectories from each of the other

cells on the surface. Second, receptor number per cell (Rt) is no

longer constant but follows a Gaussian distribution amongst the

entire cell population. Each cell is given a value for Rt between

300–700 receptors upon model initialization[33]. Third, complex

degradation rate (kdeg) is included as a parameter as it known that

Jak-Stat pathway stimulation can induce lysosome-dependent

receptor degradation[34,35]. With these modifications in place,

complex number for cell i,(Cn)i, is calculated by summing the

autocrine trajectories (Piau) with the paracrine trajectories from

other cells on the surface (Pipara). This sum is then multiplied by the

ligand secretion rate (vl) minus the degradation rate (kdeg) and

simulation time (t) to obtain (Cn)i:

Cnð Þi~ vl{kdeg

� �Pi

auzPipara

� �t ð5Þ

Piau~

vDai

vDaiz

4

p

ð6Þ

Pipara~prcell

2Xn

j~1;j!~i

p rij

� �2prij

ð7Þ

p rij

� �~

2keffi

pD

ð?

0

x2

keffirij

�D

� �2zx2

K0 xð Þdx ð8Þ

Note that Da and keff must be solved for each cell i because

receptor number per cell is no longer constant. Thus, equations (1)

and (2) have to be calculated with the corresponding receptor

number to become equations (6) and (8) respectively.

The use of equations (5)–(7) requires additional assumptions not

present in the original model that need to be highlighted. First, the

spatial probability density of traps is assumed to be the same for a

Engineering Stem Cell Colonies


random configuration and an array of colonies at the same

fractional coverage. This assumption is equivalent to saying that

keff remains constant for a cell population distributed randomly

across the surface or ‘‘micro-patterned’’ into a single corner.

Second, the complex degradation rate (kdeg) is assumed to be

constant with respect to time and independent of the number of

complexes. Last, it is assumed that distances between cells can be

segregated into 1 mm increments (i.e. 1/10th cell width). This

assumption is made to simplify the calculation of equation (8).

Equation (8) is calculated in 1 mm increments for all distances

ranging from the minimum and maximum cell separation

distances, i.e. 2*rcell and 21/2*(culture width) respectively. This

allows trapping density probabilities to be tabulated, saving

computation time. The parameters for the simulation were chosen

based upon previous work done on the gp130-Jak-Stat signaling

pathway or in similar systems (Table 1).

Results

Theoretical prediction of endogenous signalingactivation in non-patterned versus micro-patterning cellcultures

As an initial step to determine the effect of micro-patterning on

cell cultures, the average number of complexes per cell (Cn) was

calculated as a function of cell surface coverage under non-

patterned and patterned conditions (Fig. 1). Cells were assumed to

be flat disks with a radius of 5 mm and the cell culture area to be

0.3 cm2 (approximately the bottom of a well in a 96-well plate).

Cell surface coverage was the ratio of the area occupied by cells to

the total cell culture area. In the non-patterned case, cells were

randomly given non-overlapping spatial co-ordinates until the

appropriate cell surface coverage was attained. In the patterned

case, cells were grouped into a square-packed arrangement

keeping the cell surface coverage constant. Cn was then calculated

for each cell in both spatial arrangements and visually represented

using a heat map (Fig. 1A). The average Cn for each spatial

arrangement was then calculated for cell surface coverages ranging

from 0.1 (10% confluent) to 0.8 (80% confluent) (Fig. 1B).

Interestingly, the random spatial configuration exhibited a linear

increase in Cn with cell surface coverage while the micro-patterned

arrangement had a logarithmic trend. This indicated that there is

a window of opportunity for micro-patterning to affect Cn between

0.1 and 0.8 surface fractional coverage. The upper limit is intuitive

as cultures upon reaching confluence effectively have the same

local cell density as a patterned culture. Using a previously

published model[29], a correlation between Cn and nuclear pStat3

accumulation was predicted (Fig. 1C). This plot was generated in

order to validate the model using a metric that is easily measured

experimentally, i.e. nuclear accumulation of pStat3 using quan-

titative immuncytochemistry. Lastly, the predicted nuclear accu-

mulation of pStat3 as a function of cell surface coverage was

obtained showing the same trends as Cn using the co-relation

provided (Fig. 1D).

Heterogeneity in endogenous Jak-Stat activation inmESC cultures can be predicted in silico

To demonstrate the ability of the mathematical model to predict

endogenous activation of Stat3, mESCs were seeded at densities

ranging from 10,000–40,000 cells per well in a 96-well plate and

cultured for 24 hrs. Cells were cultured in serum-free media

containing LIF, no LIF, and 600 nM Jak inhibitor (JakI) to inhibit

the pathway. The cells spontaneously produced colonies with an

average size that depended upon the initial seeding density

(Fig. 2A). The average single-cell pStat3 activation was calculated

for each treatment and seeding density (Fig. 2B). Statistical

significance (p,0.05) was found between treatment conditions for

a given cell seeding density. However, no significance was found

(p.0.4) between cell seeding densities for a given treatment. As a

quantitative metric of the microenvironment, the local cell density,

defined as the number of neighbours cells within a 400 mm was

calculated using a previously developed algorithm, Neighbours

Analysis (Supplementary Fig. S2). By using this algorithm, heat

maps (Fig. 2C) and histograms (Fig. 2D) were generated to analyze

the variance in local cell density in each well. Mean localized cell

density increased with higher seeding densities as expected.

However, the histograms also indicate that increasing seeding

density leads to a broader distribution of localized cell densities

across the well. Such an observation suggests that one can attempt

to increase paracrine signaling by simply seeding more cells into

the well, however, such an action will simultaneously increase the

heterogeneity within the well by creating multiple local microen-

vironments consisting of a differing number of cells.

Consequently, after measuring nuclear levels of activated Stat3

(pStat3) at the single cell level, no statistically significant increase in

pStat3 was found as a function of initial cell density (cells/well).

Conversely, if the localized cell density is incorporated into the

measurement such that the single cell nuclear pStat3 levels are

averaged across all cells having the same localized cell density,

statistically significant increases in pStat3 as a function of localized

cell density could be found (Fig. 2E, i and ii). Furthermore, the

increase in pStat3 can be predicted by inputting the spatial

arrangement of cells into the mathematical model (Fig. 2F). Note

Table 1. Parameters used in simulations.

Parameter Description Value Unit Ref.

Rt Total receptors per cell 300–700 # [33]

rcell Radius of a cell 10 um Empirically determined

D Diffusivity of LIF (actually IL-6) 2.761027 cm2/s [47]

kon Association rate constant 0.26109 M21min21 [29]

koff Dissociation rate constant 0.0011 min21 [29]

ke Endocytosis rate constant 0.0099 min21 [29]

kdeg Degradation rate of complexes 0.2 min21 [29]

vl Ligand secretion rate 0.831 #/min [48]

time Simulation time 24 hrs

doi:10.1371/journal.pone.0006438.t001



that without including information about the local cell density of a

cell, simple quantitative immunocytochemistry would yield the

erroneous conclusion that cell density does not affect pStat3

activation. The above analysis suggests that it is indeed the local

microenvironment of a cell, rather than the well’s macroscopic

environment, that correlates with endogenous signal activation of

a single-cell. Moreover, the effect of the local microenvironment

can be adequately described by the mathematical model provided.

Interestingly, gradients in pStat3 were greater in the presence of

supplemental LIF (500 pM) compared to non-LIF supplemented

cells (Figure 2E), suggesting that pStat3 activation is not saturated

upon LIF addition. In previous work, it has been demonstrated

that there is a non-LIF autocrine factor that signals through the

gp130 receptor family that regulates pStat3 activation in the

presence of 500 pM LIF[36]. Furthermore, it has been shown that

signaling in the gp130-Jak-Stat pathway in mESCs exhibits a

switch-like response to LIF, as a consequence of a positive

feedback loop that controls transcription of signaling pathway

components[18,29]. The effect of this autoregulatory behaviour is

that sufficiently high exogenous LIF maintains pathway respon-

siveness, i.e. the ‘‘on’’ state, whereas low concentration or no

exogenous LIF cause ESCs to adopt a state of weak responsive-

ness, i.e. ‘‘off’’ state. Thus, the smaller absolute gradients in pStat3

seen in conditions without LIF may be due to the down-regulation

of pathway components due to decreased responsiveness to the

pathway.

To interrogate this possibility, we examined the sensitivity of

pStat3 gradients to the changes in expression of gp130-Jak-Stat

pathway components in silico. Three model parameters were

varied: endogenous ligand secretion, receptor number (Rt), and

the introduction of an autoregulatory positive feedback loop that

increased Stat3 (Supplementary Fig. S3) {Davey, 2007 #23}.

Note that the feedback loop was incorporated into this model by

re-calculating pStat3 as a function of Cn as originally shown in

Figure 1D but with positive feedback. These in silico experiments

suggests that the relative changes in pStat3 gradients with respect

to localized cell density will increase in the presence of the postitive

feedback loop. As exogenous LIF has been shown to increase

endogenous gp130 ligand production and receptor number, only

the autoregulatory positive feedback loop can account for the

relative increase in pStat3 with local cell density [18]. An increase

in the other two variables, ligand secretion and receptor number,

would decrease these relative changes (not observed). Consequent-

ly, the differences in the pStat3 gradients observed between the

500 pM LIF and no LIF conditions can be attributed to the

amount of Stat3 in the cell as regulated by the autoregulatory

feedback loop present in mESCs rather than extra-ceullar ligand

availability or receptor number.

Restricting colony diameter controls the activation of theJak-Stat pathway

The preceding analysis suggests that endogenous activation of

Stat3 for a single cell correlates with the localized cell density of its

immediate microenvironment. This observation allows for hy-

pothesis that micro-patterning mESCS which provides control

Figure 1. Theoretical prediction of how endogenous signaling activation increases upon micro-patterning cell cultures. A) Visualheat maps indicating the intensity and distribution of the number of bound ligand-receptor complexes (Cn) as a function of cell surface coverage. Cellsurface coverage is defined as the ratio between the area occupied by cells and the total cell culture area. As cell density or surface coverageincreases, the average Cn increases. B) Quantification of the average Cn per cell as a function of cell surface coverage comparing the patterned andnon-patterned cases. There is a window of opportunity between 0.1 and 0.8 cell surface coverage in which patterning will increase the average Cn percell in a colony. C) Co-relation between Cn and the levels of nuclear pStat3 in a single cell as predicted by a previously published model[29]. D)Quantification of the predicted nuclear pStat3 accumulation in cells as a function of cell surface coverage.doi:10.1371/journal.pone.0006438.g001



over the local cell density by restricting colony diameter (D),

separation (pitch, P), and degree of clustering, can modulate

endogenous Stat3 activity. To test this hypothesis, mESCs were

patterned in colony sizes ranging from 50–200 um with pitch

ranging from 200–400 um (Supplementary Fig. S4), in a serum-free

media with and without 500 pM exogenous LIF supplementation

and the average local cell density was obtained for each patterned

culture (Fig. 3A). The coefficient of variance in the distribution of

local cell densities in micro-patterned colonies was found to be lower

than in the non-patterned cultures. Once again, pStat3 activation in

these micro-patterned colonies was predicted accurately by the

model (Fig. 3B). Oct-4 expression in ESCs correlated with pStat3

activation and colony diameter (Fig. 3C), indicating that even after

only 24 h of culture cells are already responding to the signaling

gradients established using our spatial segregation strategy.

Differences in pStat3 activation were accentuated with 500 pM

LIF supplementation (black bars) and diminished in 600 nM JAKI.

This data suggests that the autoregulatory postitive feedback loop

maintained by exogenous LIF is maintained in micropatterned

cultures and are JAK dependent. Differences in Oct-4 levels

amongst micro-patterned colonies were minimal with exogenous

LIF supplementation and JAKI.

Figure 2. Model validation using the activation of the Jak-Stat signaling pathway in non-patterned mouse ESCs cultures. A) Single-field Hoechst 33342 micrographs of mESCs seeded at various densities in 96-well plates after being cultured for 24 hours in serum-free media withoutLIF. Scale-bar is 200 mm. B) Quantification of the average single-cell nuclear pStat3 accumulation after 24 hours of culture. While differences betweenexogenous supplementations were statistically significant at a given seeding density (p,0.05), no significance was present between cell seedingdensities (p.0.40) for a given treatment. C) Heat maps indicating the increase and distribution in local cell density which is defined as the number ofneighbouring cells within a 400 mm radius. The local cell density was calculated using an already published algorithm (See Materials andMethods)[46]. D) Histogram for the localized cell density as a function of initial seeding density showing that as initial cell density increases, the meanlocalized cell density increases and the distribution of cell densities broaden. E) Quantification of pStat3 signaling gradients present within singlewells using the Neighbours Analysis algorithm. At low seeding densities, 10,000 cell/well (E–i), pStat3 can be seen to be increasing with cell densityonly in the presence of LIF whereas at a higher cell density, 40,000 cells/well (E–ii), signaling gradients can be found under all three treatments. Errorbars represent the SEM of 9 wells cultures in three separate trials. F) Predicted and actual measurements of single-cell pStat3 levels as function oflocalized cell density demonstrating that the mathematical model developed in this study can be used to predict spatial fluctuations in signalactivation in the Jak-Stat in mESCs. Data for this plot is taken for a single 96-well plate seeded at 40,000/well in the no LIF condition. While thepercent increase in pStat3 in this figure is approximately 40%, the range in percent change is 10–40% for n = 9 wells. Error bars represent the SEM ofat least 500 cells within each bin along the x-axis.doi:10.1371/journal.pone.0006438.g002



Figure 3. Modeling predicts the endogenous activation of Stat3 as regulated by the spatial organization of mESC cultures. In the firstexperiment (A–C), mESCs were cultured in colonies with diameters (D) ranging from 50–200 mm. Pitch (P), the centre-to-centre distance betweencolonies, was kept constant for the two smaller colonies and was increased to accommodate the larger D = 200 mm colony. A) Average local celldensity calculations for each pattern type demonstrating the co-relation between colony diameter and the local cell density. B) Predicted and actualmeasurements of single-cell pStat3 levels as function of increasing colony diameter. White bars indicate cultures with no LIF supplementation, blackbars with 500 pM LIF supplementation, and grey bars with 600 nM JAK inhibitor. C) Experimental measurements of Oct-4 intensity demonstrated theco-relation between colony diameter, pStat3, and Oct-4 expression with LIF deprivation. In the second experiment (D–F), mESCs were cultured incolonies with fixed diameter, D = 100 mm, while pitch (P) ranged from 200-500 mm. D) Average local cell density calculations for each pattern typedemonstrating the inverse co-relation between pitch and the local cell density. E) Predicted and actual measurements of single-cell pStat3 levels asfunction of decreasing pitch. F) Experimental measurements of Oct-4 intensity demonstrated the inverse co-relation between pitch and Oct-4expression. In the last experiment (G–I), both colony diameter and pitch were altered to increase the degree of clustering while keeping the totalavailable area for seeding constant. G) Average local cell density calculations for each pattern type demonstrating that the local cell density waswithin experimental error between pattern types. H) Predicted and actual measurements of single-cell pStat3 levels as function of degree ofclustering. I) Experimental measurements of Oct-4 intensity demonstrating a positive co-relation between pStat3 and Oct-4. Representative brightfield images of micro-patterned mESC cultures can be found in Supplementary Fig. S4.doi:10.1371/journal.pone.0006438.g003



Increasing pitch decreases activation of the Jak-Statpathway

We next wanted to demonstrate the effect of increasing colony

separation (pitch) on endogenous Stat3 activation. MESCs were

cultured with colony diameters fixed to D = 100 um and pitch

ranging from P = 100–500 um. As expected, Neighbours Analysis

indicated that as pitch increased the local cell density decreased

(Fig. 3D). Nuclear pStat3 values were inversely proportional to pitch

and were successfully predicted by the model (Fig. 3E). Likewise,

Oct-4 expression decreased with increasing pitch. Exogenous LIF

supplementation and JAKI had the same effect on pStat3 and Oct-4

as the previous micro-patterning experiment. Interestingly, there is

no statistically significant difference in pStat3 from P = 400 um to

P = 500 um suggesting that the colonies are independent of each

other at this distance with respect to diffusible ligands that activate

Stat3 as a downstream effector (Fig. 3F). Notably, the only extrinsic

parameter of the culture changing in these experiments is pitch.

Critically, the number of cell-cell contacts and distribution of

mechanical forces within a colony can be presumed to be constant.

As phenotypic changes in micro-patterned cultures could be

attributed to either to a change in receptor-ligand binding, the

number of cell-cell contacts, or distribution in mechanical forces,

this experiment serves as an important control by keeping the latter

two variables constant. Thus, this experiment demonstrates that

micro-patterning can alter soluble ligand availability and binding.

Increasing degree of clustering increases the activationof the Jak-Stat pathway

The last micro-fabrication parameter interrogated was degree of

clustering. This experiment was conducted to further isolate the

effect of spatial organization independent of any effects of overall

cell density. In this study, colony diameter ranged from D = 50–

200 mm and pitch from P = 200–800 mm, while keeping overall

seeding area (and cell number) constant between pattern

arrangements. The local cell density was calculated, using

Neighbours Analysis, to be within experimental error for all three

pattern types (Fig. 3G). With no LIF supplementation, levels of

pStat3 increased with degree of clustering which was predicted by

the model (Fig. 3H). Furthermore, Oct-4 expression increased with

degree of clustering (Fig. 3I). As seen before, 500 pM LIF

supplementation increased the relative change in pStat3 amongst

micropatterns however, Oct-4 did not change significantly as Stat3

levels are above the differentiation threshold. JAKI had a similar

effect as before by diminishing the observed differences. Note that

increasing the degree of clustering was not as effective as doubling

colony size in regulating pStat3 levels. These latter experiments,

nonetheless, provide further support that the micro-organization

of a culture can regulate the activation of an endogenous signaling

pathway since the number of cells in the well and local cell density

in each pattern type was approximately the same. Note that even

though the local cell densities were similar, the average separation

between cells would decrease as degree of clustering increases.

This experiment demonstrates that paracrine activation of the Jak-

Stat pathway is both cell-number and cell-separation dependent.

In order to reveal localized intra (within)-colony effects in the

activation of the Jak-Stat pathway additional analysis was on

performed on the D = 50 um and D = 100 um colonies (Supple-

mentary Figure S5). This analysis demonstrated a radial

dependence in pStat3 but not Oct-4. This data suggests that

internal cells have increased activation of Stat3 relative to outer

cells. Relative changes in Oct-4 may not be apparent, likely due to

short-time frame of the experiment (24 hours); loss of pStat3

responsiveness has been shown to precede the loss of Oct-4[18].

Micro-patterning mESC cultures regulates transcriptionof known direct and indirect Stat3 targets

After establishing that micro-patterned ESCs exhibit different

signaling levels of Stat3 activation, we next sought to determine if

this change in signaling activation had downstream consequences

on known and predicted targets of Stat3. We hypothesized that

Stat3 targets would be differentially expressed in small (D = 50 mm,

P = 200 mm) colonies versus large (D = 200 mm, P = 400 mm)

colonies because of the lower levels of pStat3 in small colonies.

Quantitative real-time PCR (qRT-PCR) was used to quantify the

expression of several possible targets of Stat3 (Fig. 4). As expected

the pluripotency genes decreased with smaller colony size. The

expression of Jak-Stat pathway members decreased as well in small

colonies, an observation which is to be expected by the auto-

regulatory behavior of this pathway as revealed previously in non-

patterned cultures[18]. Genes related to the epigenetic status of

mESCs including Jmid1a, Dnmt1, and HP1 decreased as well.

These findings provide further evidence that spatial control over

Stat3 and its downstream transcriptional targets can be achieved in

micro-patterned mESC cultures with implications to the pluripo-

tency network and epigenetic state of the mESCs.

Discussion

Local gradients of activated signaling molecules exist in ESC

cultures and these gradients correlate with the expression of

pluripotent stem cell markers such as Oct-4 and Nanog. These

gradients can be correlated to the localized cell density in a single well

as well as manipulated directly using micro-patterning technologies.

Specifically, by altering spatial arrangement, we have shown for the

first time that altering colony diameter, the distance between colonies,

and the degree of clustering of a culture can modulate in a predictive

manner the nuclear levels of pStat3 in mESCs (Fig. 5).

To date, micro-scale approaches to modulating cell-ECM

interactions and cell-cell contacts have provided insight into how

in vitro niches can regulate stem cell fate. For instance, micro-

patterning single adult mesenchymal stem cells on defined ECM

substrates has been shown to provide inductive cues to regulate

differentiation into osteoblasts or adipocytes through regulation of

cytoskeleton tension[37]. Likewise, patterning mESCs in bow-tie

micro-wells has demonstrated the effect of cell-cell interactions on

neuroectoderm differentiation, perhaps through a mechanism

involving connexin-43[38]. Interestingly, another study that

cultured hepatocytes on micromachined silicon substrates capable

of regulating cell-cell contacts dynamically demonstrated that both

cell-cell contact and newly identified paracrine factor were required

to retain the hepatocyte phenotype[39]. The activity of this

paracrine factor, similar to the Stat3 activity demonstrated here,

was limited to ,400 mm. Thus, it is likely that the mathematical

model and experimental approach used in this work are applicable

to a wide range of signaling pathways in multiple cells types.

Modeling using finite element analysis has been used to predict

the effects of micro-patterning on multi-cellular aggregates[40].

This previous work has revealed that regions of higher actin

cytoskeleton tension can appear at edges of colonies leading to

proliferative foci of endothelial cells. The mathematical model

presented here complements this study by providing a model

capable of describing soluble paracrine activity on micro-patterned

multi-cellular aggregates. Developing in silico tools such as these to

predict how cellular behaviour changes as a function of spatial

organization will allow for mechanism-based design of lab-on-chip

devices to regulate stem cell fate.

In this work, we have shown that small mESC colonies

(D = 100 mm) are independent of each other at a distance of 400–



500 mm and that D = 100 mm colonies are sufficiently large to

maximize endogenous Stat3 activation. These observations can be

explained by the manipulation of the diffusion and binding of locally

secreted cytokines of the IL-6 family, as well as the responsiveness of

mESCs to these ligands[36]. We have previously associated the

secretion and responsiveness to IL-6 ligands to a fixed-location

independent auto-regulatory niche (FLIAN) produced by mESCs to

temporarily maintain self-renewal in the absence of LIF. MESCs

are known to express several members of the IL-6 family of

cytokines including IL-6, IL-11, LIF, oncostatin m, cardiotrophin-1

(CT-1), cardiotrophin-like cytokine (CLC), and ciliary neurotrophic

factor (CNTF). These factors may contribute to the paracrine

activation of the Jak-Stat pathway to maintain mESCs. Our results

show that maximizing paracrine activation of endogenous signals

cannot simply be achieved by seeding more cells in well without

increasing the heterogeneity of a culture. Micro-patterned cultures

regulate endogenous signaling without this side-effect.

Indeed, there are two critical differences between non-patterned

and micropatterned cultures with respect to increased paracrine

signaling. The first difference is that the model predicts a greater

change in complex number (Cn) and pStat3 under a wide range of

cell densities when cells are patterned (logarithmic trend) versus non-

patterned (linear trend) (Figure 1B and 1D). The reason for these

trends is likely due to the increased cell clustering that occurs when

cells are patterned, i.e. at the same local cell density cells in a

micropatterned colony are all together whereas in non-patterned

cultures non-uniformities occur. The second difference is that the

non-uniformity in non-patterned seeded cultures leads to a systemic

error in measurement. As the culture arrangement moves from a

non-patterned to patterned spatial arrangement, several factors

change including colony size, separation, and degree of clustering. In

the case of Figure 2, the output (pStat3) represents the average over

all these different microenvironments leading to a systemic error in

the measurement. Consequently, the differences between cells would

be under-represented. Indeed, the differences in paracrine signaling

within non-patterned versus patterned cultures highlights the

importance of our approach or other micro-patterning technologies

in teasing apart these variables in the local microenvironment.

Interestingly, paracrine signaling of bone morphogenetic protein

2 (BMP2) has been implicated in regulating the niche-size of

hematopoietic stem cells in vivo (HSCs)[41]. Moreover, other studies

have shown that ectopically regulating the quantitative levels of Oct-

4 can be used to induce trophectoderm and primitive mesoderm/

endoderm[42]. Thus, the methodologies developed in this work

allow for the systematic prediction, detection, and manipulation of

endogenous signaling gradients in vitro along similar mechanisms

found in vivo in order to regulate cell fate.

Materials and Methods

Micro-contact printing of ECM onto tissue culturesubstrates

Poly(dimethylsiloxane) (PDMS) stamps with feature sizes of 50–

200 mm (D, diameter) and distance between features between

Figure 4. Micro-patterning mESC cultures regulates transcription of known Stat3 targets. Quantitative real-time PCR results of mESCscultured on D = 50 mm, P = 200 mm and D = 200 mm, P = 400 mm pattern cultured for 24 hours in serum-free media without LIF. The data isnormalized to the house-keeping gene GAPDH and each gene is plotted relative to its expression found in the D = 200 mm, P = 400 mm pattern.Pluripotency-associated genes, Oct-4, Nanog, Klf4, and Sal4 decrease in expression with smaller colony size. Likewise, members of the Jak-Statpathway, including LIF receptor (LIFR), gp130, Stat3, and Socs3 all decrease with smaller colony size demonstrating spatial control of this auto-regulatory pathway. C-myc, another target of pStat3 is also down-regulated in small colonies. Epigenetic modifiers of mESCs including HP1, Dmnt1,and Jmd1ja are also down-regulated in small colonies. Error bars represent the S.E.M for n = 3 biological replicates. Asterisks indicate statisticalsignificance of p,0.05 by the t-test.doi:10.1371/journal.pone.0006438.g004



200–800 mm (P, pitch) were fabricated using standard soft

lithography protocols provided elsewhere. The micro-contact

printing protocol was based upon a technique detailed in other

work[43]. Briefly, PDMS stamps were inked with a solution of

25 mg/mL of bovine fibronectin and 50 ug/mL of bovine gelatin

prepared in sterile ddH2O for 1 hr. After inking, stamps were

rinsed thoroughly in sterile ddH2O and dried with N2 gas. The

stamps were then placed quickly onto tissue culture-treated plastic

slides and placed in a humidity chamber with a relative humidity

between 55–70% for 10 min. Silicone gaskets, 20 mm in diameter

and 2.5 mm in height, were placed around the patterned regions

to create a leak-proof well. Slides were passivated in 5% PluronicTM F-127 for 1 hr to prevent non-specific attachment.

Cell CultureR1 mouse embryonic stem cells were maintained at 370 C in

humidified air with 5% CO2 in ESC culture medium comprised

80% Dulbecco’s Modified Eagle Medium (DMEM, Gibco-BRL,

Rockville, MD) supplemented with 15% ESC qualified fetal

bovine serum (FBS), 100 U/mL (Gibco-BRL), 50 ug penicillin-

streptomycin (Gibco-BRL), 2 mM L-glutamine (Gibco-BRL),

0.1 mM 2-mercaptoethanol (Sigma, St. Louis, MO), and

500 pM leukemia inhibitory factor (LIF, Chemicon, Temecula,

CA). Culture flasks (Sarstedt, Newton, NC) were prepared prior to

cell seeding by coating with a solution of 0.2% bovine gelatin

(Sigma) in phosphate buffered saline (PBS, Gibco-BRL). All ESCs

were used between passages 15–30. Under experimental condi-

tions, the above media was used with 15%KNOCKOUTTM

serum replacement (KOSR, Invitrogen) as a substitute for

15%FBS.

Seeding of mESCs on non-patterned substratesSingle cell suspensions were generated by incubating with

0.25%trypsin with 1 mmEDTA (Trypsin-EDTA, Gibco-BRL) for

3 minutes and then quenching with ESC culture media. Cells were

then resuspended in ESC culture media with 500 pM LIF

containing 15%KOSR instead of 15%FBS. Cells were re-

suspended at various concentrations ranging from 10000–40000

cell per well (volume/well = 200 uL). Cells were seeded into a

tissue-culture treated 96-well plate coated overnight at 37uC with

25 ug/mL of bovine fibronectin and 50 ug/mL of bovine gelatin

in ddH2O. Five replicates per concentration was used. Cells were

seeded in KOSR-based media with LIF for 4 hrs, after which

media was replaced with KOSR-based media with or without

500 pM LIF or 600 nM of Jak inhibitor 1 (Calbiochem). Cells

were fixed and stained 20 hrs after media exchange.

Culture of mESCs on non-patterned substratesSingle cell suspensions were generated by incubating with

0.25%trypsin with 1 mmEDTA (Trypsin-EDTA, Gibco-BRL) for

3 minutes and then quenching trypsin with ESC culture media.

Cells were then resuspended in ESC culture media with 500 pM

LIF containing 15%KOSR instead of 15%FBS. Cells were re-

suspended at various concentrations ranging from 10000–40000

cell per well (volume/well = 200 mL). Cells were seeded into a

tissue-culture treated 96-well plate coated overnight at 37uC with

Figure 5. Micro-patterning mESC cultures provides spatial control over endogeneous Jak-Stat activation. Using in silico models andexperimental validation, we have demonstrated that endogenous activation of the Jak-Stat pathway can be regulated spatially by micro-patterningmESC cultures. Three parameters were explored: increasing colony diameter, decreasing colony pitch, and increasing the degree of clustering.doi:10.1371/journal.pone.0006438.g005



25 mg/mL of bovine fibronectin and 50 mg/mL of bovine gelatin

in ddH2O. Five replicates per concentration was used. Cells were

seeded in KOSR-based media with LIF for 4 hrs, after which

media was replaced with KOSR-based media with or without

500 pM LIF or 600 nM of Jak inhibitor 1 (Calbiochem). Cells

were fixed and stained 20 hrs after media exchange.

Culture of mESCs on patterned substratesSingle cells suspensions were created in a similar manner as the

non-patterned case at a density of 2.506105 cells/well (volu-

me = 750 uL) and incubated with the patterns for 4 hrs. The cells

were washed with KOSR-based media without LIF three times to

remove non-attached cells and then incubated in KOSR-based

media without LIF for an additional 20 hrs. The seeding density

chosen was empirically determined to ensure that the colonies

were confluent and still a monolayer 24 hours later.

Quantified immunohistochemistry of patterned and non-patterned substrates

Patterned and non-pattered cultures were fixed in 3.7%

formaldehyde in PBS for 15 min at 37uC and were washed three

times with PBS. Samples were then permeabilized in 100%

methanol for 2 min, washed three times with PBS and incubated

with blocking buffer consisting of 10%FBS in PBS overnight at

4uC. Samples were then stained with mouse anti-Oct-4 primary

antibody (1:200) and rabbit anti-pStat3 primary antibody (1:200)

diluted blocking buffer overnight at 4uC. Cells were washed 3x

with PBS and incubated with goat anti-mouse IgG Alexafluor 488

secondary antibody (1:200), goat anti-rabbit IgG Alexafluor 647

secondary antibody (1:200), and Hoechst 33342 (0.1 ug/mL) to

identify all nuclei. After staining, samples were imaged using the

Cellomics Arrayscan VTI high-throughput fluorescence micro-

scope using the Target Activation algorithm to provide quantified

image analysis of the spatial location of the centroid of the nucleus

relative to the centre of the image as well as fluorescence intensity

measurements for pStat3 and Oct-4 in the nucleus. To get colony

area and the number of cells per colony, the Morphology

ExplorerTM image analysis algorithm available through the

ArrayscanVTI software was used.

Calculation of the local cell density using NeighboursAnalysis

Neighbours analysis is a set of algorithms developed in the

PythonTM scripting language used to calculate the number of

neighbours a cell has within an arbitrary radius as well as the

average distance of those neighbours to that cell (Fig. S2). The

input to the algorithm is the spatial location of the nucleus relative

to the centre of the image as provided by the Cellomics Target

Activation Algorithm. Any software which gives the x-y coordi-

nates and single-cell quantification of fluorescence could be used.

Based on systems specifications of the microscope, well size, and

the magnification of the objective lens, the program re-constructs

the entire well. For every cell, the Euclidean distance between itself

and all the other cells in well was calculated and the number of

immediate neighbours was gated upon neighbours cells that were

found within an arbitrary radial threshold of 400 mm. The number

of neighbours within a 400 mm is the localized cell density of the cell.

This threshold was found empirically by plotting nuclear pStat3 as

a function of radial thresholds from 100–1000 mm and choosing

the highest radial threshold that had a significant co-relation (R2).

These radial thresholds provide an approximate size to the stem

cell niche in spatial terms by suggesting that cells within a certain

proximity are those that are most effective in communicating with

a cell and thereby influencing its fate.

Quantitative RT-PCR analysisRNA from patterned mESC cultures was isolated using TRIzol

reagent (Invitrogen) and purified including DNA digestion using

the RNEasy Mini Kit (Qiagen) according to the manufacturers’

protocols. cDNA synthesis was carried out using the Superscript

First Strand Synthesis system (Invitrogen). Reaction conditions for

the PCR reaction were incubation at 94uC for 10 min, followed by

40 cycles of 94uC for 30 s, 60uC for 30 s, and 72uC for 30 s using

the Applied Biosystems 7900HT Fast Real-time PCR System. A

table of the primers used can be found in the Table S1 [44,45].

Model CodeThe entire model code was programmed in the Python scripting

language (http://www.python.org). Visualization of gradients

using chloropleth maps was implemented using the Python

Imaging Library (PIL).

Statistical AnalysisAll data are reported as mean6standard deviation. Experiments

measuring differences between means were assessed using paired

Student’s t-test with * indicating p,0.05 and ** indicating p’0.01.

Experiments testing variances were assessed using the f-test.

Supporting Information

Table S1 Primers used in this study.

Found at: doi:10.1371/journal.pone.0006438.s001 (0.03 MB

DOC)

Figure S1 Schematic of the spatial parameters of the model.

Complex number for cell i (Cin) is proportional to the sum of the

probabilities of ligand capture by autocrine trajectories (Piau)and

paracrine trajectories (Pipara) that are dependent of the radius of

the cell (rcell). Pipara is determined by summing the paracrine

contributions of each cell pair in the well p(rij)which has radial (rij)

and cell density (s) dependencies.

Found at: doi:10.1371/journal.pone.0006438.s002 (0.04 MB TIF)

Figure S2 Development of the Neighbours Analysis algorithm.

A) Photo-micrographs at 10X of non-patterned mESCs immuno-

stained with Hoechst 33342, Oct-4, and pStat3 and the resulting

heat-map constructed using the Python Imaging Library (PIL).

Masks around individual nuclei were drawn using the Target

Activation algorithm. B) Schematic of the Neighbours Analysis

algorithm which counts the number of cells within a 400 mm

radius which is dubbed local cell density. C) Example of binning

for cells seeded at a density of 40,000 cells per well. D)

Representative pStat3 histograms for cells after binning. E)

Summary plot for data taken from the histograms to illustrate

co-relations between pStat3 and local cell density.


Figure S3 Theoretical predictions of how Jak-Stat pathway

signaling components affect local signaling gradients. A) In these in

silico experiments, the spatial arrangement of mESCs cultured in a

single 96-well (approximately 35,000 cells) were inputted to the

model. The predicted increase in pStat3 with increased localized

cell density was computed while varying three parameters:

endogenous ligand secretion, receptor number (Rt), and the

presence of a positive feedback loop that increases total Stat3

(C). A) Predicted gradients in Stat3 as a function of increasing

ligand secretion. According to model data, the relative changes in

Stat3 that co-relate with localized cell density decrease moderately

with increased ligand secretion. B) Predicted gradients in Stat3 as a

function of increased receptor number (Rt). Increasing receptor

number decreases the relative change in Stat3 with localized cell



density. C) Predicted gradients in Stat3 in the presence of an auto-

regulatory positive feedback loop that increases Stat3 in mESCs.

This feedback loop has been previously described and mod-

elled[18,29]. The effect of this loop is to accentuate the effect of

localized cell density on Stat3 gradients present in the culture.


Figure S4 Bright field images of micro-patterned colonies.

Bright field images of mESCs seeded in six different pattern types

after 24 hours of culture in serum-free media without LIF. Scale-

bar is 200 mm.


Figure S5 Radial organization of pStat3 within colonies. To

reveal intra-colony variation in protein expression or signal

activation, single-cell pStat3 and Oct-4 expression was plotted as

a function of distance from the centre of the colony. mESCs were

patterned in D = 50 mm, P = 200 mm and D = 100 mm,

P = 200 mm arrangements. A) A radial dependence in pStat3

was observed in in both D = 50 mm and D = 100 mm colonies. B)

No radial depense in Oct-4 was observed suggesting that the

timeframe of the experiment (16 hours) was too short to reveal

significant changes in Oct-4 expression.


Author Contributions

Conceived and designed the experiments: RP AM EK PWZ. Performed

the experiments: RP KO AM. Analyzed the data: RP KO. Contributed

reagents/materials/analysis tools: EK PWZ. Wrote the paper: RP PWZ.

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manipulation of signaling thresholds in “engineered stem cell niches” identifies design criteria...

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